def get_example_tree():
# Set dashed blue lines in all leaves
nst1 = NodeStyle()
nst1["bgcolor"] = "LightSteelBlue"
nst2 = NodeStyle()
nst2["bgcolor"] = "Moccasin"
nst3 = NodeStyle()
nst3["bgcolor"] = "DarkSeaGreen"
nst4 = NodeStyle()
nst4["bgcolor"] = "Khaki"
t = Tree("((((a1,a2),a3), ((b1,b2),(b3,b4))), ((c1,c2),c3));")
for n in t.traverse():
n.dist = 0
n1 = t.get_common_ancestor("a1", "a2", "a3")
n1.set_style(nst1)
n2 = t.get_common_ancestor("b1", "b2", "b3", "b4")
n2.set_style(nst2)
n3 = t.get_common_ancestor("c1", "c2", "c3")
n3.set_style(nst3)
n4 = t.get_common_ancestor("b3", "b4")
n4.set_style(nst4)
ts = TreeStyle()
ts.layout_fn = layout
ts.show_leaf_name = False
ts.mode = "c"
ts.root_opening_factor = 1
return t, ts
def get_example_tree():
# Random tree
t = Tree()
t.populate(20, random_branches=True)
# Some random features in all nodes
for n in t.traverse():
n.add_features(weight=random.randint(0, 50))
# Create an empty TreeStyle
ts = TreeStyle()
# Set our custom layout function
ts.layout_fn = layout
# Draw a tree
ts.mode = "c"
# We will add node names manually
ts.show_leaf_name = False
# Show branch data
ts.show_branch_length = True
ts.show_branch_support = True
return t, ts
def get_example_tree():
# Set dashed blue lines in all leaves
nst1 = NodeStyle()
nst1["bgcolor"] = "LightSteelBlue"
nst2 = NodeStyle()
nst2["bgcolor"] = "Moccasin"
nst3 = NodeStyle()
nst3["bgcolor"] = "DarkSeaGreen"
nst4 = NodeStyle()
nst4["bgcolor"] = "Khaki"
t = Tree("((((a1,a2),a3), ((b1,b2),(b3,b4))), ((c1,c2),c3));")
for n in t.traverse():
n.dist = 0
n1 = t.get_common_ancestor("a1", "a2", "a3")
n1.set_style(nst1)
n2 = t.get_common_ancestor("b1", "b2", "b3", "b4")
n2.set_style(nst2)
n3 = t.get_common_ancestor("c1", "c2", "c3")
n3.set_style(nst3)
n4 = t.get_common_ancestor("b3", "b4")
n4.set_style(nst4)
ts = TreeStyle()
ts.layout_fn = layout
ts.show_leaf_name = False
ts.mode = "c"
ts.root_opening_factor = 1
return t, ts
def get_example_tree():
# Random tree
t = Tree()
t.populate(20, random_branches=True)
# Some random features in all nodes
for n in t.traverse():
n.add_features(weight=random.randint(0, 50))
# Create an empty TreeStyle
ts = TreeStyle()
# Set our custom layout function
ts.layout_fn = layout
# Draw a tree
ts.mode = "c"
# We will add node names manually
ts.show_leaf_name = False
# Show branch data
ts.show_branch_length = True
ts.show_branch_support = True
return t, ts
# the search_nodes method (I take only the first match )
A = t.search_nodes(name="A")[0]
# and using the shorcut to finding nodes by name
C = t & "C"
H = t & "H"
I = t & "I"
# Let's now add some custom features to our nodes. add_features can be
# used to add many features at the same time.
C.add_features(vowel=False, confidence=1.0)
A.add_features(vowel=True, confidence=0.5)
ancestor.add_features(nodetype="internal")
# Or, using the oneliner notation
(t & "H").add_features(vowel=False, confidence=0.2)
# But we can automatize this. (note that i will overwrite the previous
# values)
for leaf in t.traverse():
if leaf.name in "AEIOU":
leaf.add_features(vowel=True, confidence=random.random())
else:
leaf.add_features(vowel=False, confidence=random.random())
# Now we use these information to analyze the tree.
print "This tree has", len(t.search_nodes(vowel=True)), "vowel nodes"
print "Which are", [
leaf.name for leaf in t.iter_leaves() if leaf.vowel == True
]
# But features may refer to any kind of data, not only simple
# values. For example, we can calculate some values and store them
# within nodes.
#
# Let's detect leaf nodes under "ancestor" with distance higher thatn
# 1. Note that I'm traversing a subtree which starts from "ancestor"
def get_example_tree():
t = Tree()
t.populate(10)
# Margins, alignment, border, background and opacity can now be set for any face
rs1 = faces.TextFace("branch-right\nmargins&borders",
fsize=12,
fgcolor="#009000")
rs1.margin_top = 10
rs1.margin_bottom = 50
rs1.margin_left = 40
rs1.margin_right = 40
rs1.border.width = 1
rs1.background.color = "lightgreen"
rs1.inner_border.width = 0
rs1.inner_border.line_style = 1
rs1.inner_border.color = "red"
rs1.opacity = 0.6
rs1.hz_align = 2 # 0 left, 1 center, 2 right
rs1.vt_align = 1 # 0 left, 1 center, 2 right
br1 = faces.TextFace("branch-right1", fsize=12, fgcolor="#009000")
br2 = faces.TextFace("branch-right3", fsize=12, fgcolor="#009000")
# New face positions (branch-top and branch-bottom)
bb = faces.TextFace("branch-bottom 1", fsize=8, fgcolor="#909000")
bb2 = faces.TextFace("branch-bottom 2", fsize=8, fgcolor="#909000")
bt = faces.TextFace("branch-top 1", fsize=6, fgcolor="#099000")
# And faces can also be used as headers or foot notes of aligned
# columns
t1 = faces.TextFace("Header Face", fsize=12, fgcolor="#aa0000")
t2 = faces.TextFace("Footer Face", fsize=12, fgcolor="#0000aa")
# Attribute faces can now contain prefix and suffix fixed text
aligned = faces.AttrFace("name",
fsize=12,
fgcolor="RoyalBlue",
text_prefix="Aligned (",
text_suffix=")")
# horizontal and vertical alignment per face
aligned.hz_align = 1 # 0 left, 1 center, 2 right
aligned.vt_align = 1
# Node style handling is no longer limited to layout functions. You
# can now create fixed node styles and use them many times, save them
# or even add them to nodes before drawing (this allows to save and
# reproduce an tree image design)
style = NodeStyle()
style["fgcolor"] = "Gold"
style["shape"] = "square"
style["size"] = 15
style["vt_line_color"] = "#ff0000"
t.set_style(style)
# add a face to the style. This face will be render in any node
# associated to the style.
fixed = faces.TextFace("FIXED branch-right", fsize=11, fgcolor="blue")
t.add_face(fixed, column=1, position="branch-right")
# Bind the precomputed style to the root node
# ETE 2.1 has now support for general image properties
ts = TreeStyle()
# You can add faces to the tree image (without any node
# associated). They will be used as headers and foot notes of the
# aligned columns (aligned faces)
ts.aligned_header.add_face(t1, column=0)
ts.aligned_header.add_face(t1, 1)
ts.aligned_header.add_face(t1, 2)
ts.aligned_header.add_face(t1, 3)
t1.hz_align = 1 # 0 left, 1 center, 2 right
t1.border.width = 1
ts.aligned_foot.add_face(t2, column=0)
ts.aligned_foot.add_face(t2, 1)
ts.aligned_foot.add_face(t2, 2)
ts.aligned_foot.add_face(t2, 3)
t2.hz_align = 1
# Set tree image style. Note that aligned header and foot is only
# visible in "rect" mode.
ts.mode = "r"
ts.scale = 10
for node in t.traverse():
# If node is a leaf, add the nodes name and a its scientific
# name
if node.is_leaf():
node.add_face(aligned, column=0, position="aligned")
node.add_face(aligned, column=1, position="aligned")
node.add_face(aligned, column=3, position="aligned")
else:
node.add_face(bt, column=0, position="branch-top")
node.add_face(bb, column=0, position="branch-bottom")
node.add_face(bb2, column=0, position="branch-bottom")
node.add_face(br1, column=0, position="branch-right")
node.add_face(rs1, column=0, position="branch-right")
node.add_face(br2, column=0, position="branch-right")
return t, ts
from ete_dev import Tree
t = Tree("(A:1,(B:1,(C:1,D:1):0.5):0.5);")
# Visit nodes in preorder (this is the default strategy)
for n in t.traverse():
print n
# It Will visit the nodes in the following order:
# /-A
# ---------|
# | /-B
# \--------|
# | /-C
# \--------|
# \-D
# --A
# /-B
# ---------|
# | /-C
# \--------|
# \-D
# --B
# /-C
# ---------|
# \-D
# --C
# --D
# Visit nodes in postorder
for n in t.traverse("postorder"):
print n
# It Will visit the nodes in the following order:
# --A
# the search_nodes method (I take only the first match )
A = t.search_nodes(name="A")[0]
# and using the shorcut to finding nodes by name
C= t&"C"
H= t&"H"
I= t&"I"
# Let's now add some custom features to our nodes. add_features can be
# used to add many features at the same time.
C.add_features(vowel=False, confidence=1.0)
A.add_features(vowel=True, confidence=0.5)
ancestor.add_features(nodetype="internal")
# Or, using the oneliner notation
(t&"H").add_features(vowel=False, confidence=0.2)
# But we can automatize this. (note that i will overwrite the previous
# values)
for leaf in t.traverse():
if leaf.name in "AEIOU":
leaf.add_features(vowel=True, confidence=random.random())
else:
leaf.add_features(vowel=False, confidence=random.random())
# Now we use these information to analyze the tree.
print "This tree has", len(t.search_nodes(vowel=True)), "vowel nodes"
print "Which are", [leaf.name for leaf in t.iter_leaves() if leaf.vowel==True]
# But features may refer to any kind of data, not only simple
# values. For example, we can calculate some values and store them
# within nodes.
#
# Let's detect leaf nodes under "ancestor" with distance higher thatn
# 1. Note that I'm traversing a subtree which starts from "ancestor"
matches = [leaf for leaf in ancestor.traverse() if leaf.dist>1.0]
# And save this pre-computed information into the ancestor node
#node.img_style["bgcolor"] = random_color()
#Tree().show()
ts = TreeStyle()
ts.mode = "c"
ts.layout_fn = layout
ts.show_leaf_name = False
ts.arc_span = 340
ts.arc_start = -70
#ts.allow_face_overlap = True
#ts.show_branch_length = True
ts.draw_guiding_lines = False
ts.optimal_scale_level = "mid"
ts.extra_branch_line_color = "red"
ts.root_opening_factor = 0.50
ts.show_border = True
ts.scale = None
t = Tree()
t.populate(200, random_branches=True, branch_range=(0, 0))
t.dist = 0.0
dists = [n.dist for n in t.traverse() if n.dist != 0]
#print max(dists), min(dists)
t.write(outfile="test.nw")
#for s in [5, None]:
# ts.scale = s
# t.render("img_scale_%s.png" %s, tree_style = ts, w=600)
t.show(tree_style=ts)
n.add_face(temp_facet, 0, "aligned")
t, ts = seq_motif_faces.get_example_tree()
temp_facet = TreeFace(t, ts)
n = main_tree.add_child()
n.add_face(temp_facet, 0, "aligned")
t, ts = barchart_and_piechart_faces.get_example_tree()
temp_facet = TreeFace(t, ts)
n = main_tree.add_child()
n.add_face(temp_facet, 0, "aligned")
# Test orphan nodes and trees with 0 branch length
t, ts = Tree(), TreeStyle()
t.populate(5)
for n in t.traverse():
n.dist = 0
temp_tface = TreeFace(t, ts)
n = main_tree.add_child()
n.add_face(temp_tface, 0, "aligned")
ts.optimal_scale_level = "full"
temp_tface = TreeFace(t, ts)
n = main_tree.add_child()
n.add_face(temp_tface, 0, "aligned")
ts = TreeStyle()
t.populate(5)
ts.mode = "c"
temp_tface = TreeFace(t, ts)
n = main_tree.add_child()
from ete_dev import Tree
t = Tree('(A:1,(B:1,(C:1,D:1):0.5):0.5);')
# Visit nodes in preorder (this is the default strategy)
for n in t.traverse():
print n
# It Will visit the nodes in the following order:
# /-A
# ---------|
# | /-B
# \--------|
# | /-C
# \--------|
# \-D
# --A
# /-B
# ---------|
# | /-C
# \--------|
# \-D
# --B
# /-C
# ---------|
# \-D
# --C
# --D
# Visit nodes in postorder
for n in t.traverse("postorder"):
print n
# It Will visit the nodes in the following order:
# --A
from ete_dev import Tree
t = Tree('((H:0.3,I:0.1):0.5, A:1, (B:0.4,(C:1,D:1):0.5):0.5);')
# Create a small function to filter your nodes
def conditional_function(node):
if node.dist > 0.3:
return True
else:
return False
# Use previous function to find matches. Note that we use the traverse
# method in the filter function. This will iterate over all nodes to
# assess if they meet our custom conditions and will return a list of
# matches.
matches = filter(conditional_function, t.traverse())
print len(matches), "nodes have ditance >0.3"
# depending on the complexity of your conditions you can do the same
# in just one line with the help of lambda functions:
matches = filter(lambda n: n.dist > 0.3 and n.is_leaf(), t.traverse())
print len(matches), "nodes have ditance >0.3 and are leaves"
def get_example_tree():
t = Tree()
t.populate(10)
# Margins, alignment, border, background and opacity can now be set for any face
rs1 = faces.TextFace("branch-right\nmargins&borders",
fsize=12, fgcolor="#009000")
rs1.margin_top = 10
rs1.margin_bottom = 50
rs1.margin_left = 40
rs1.margin_right = 40
rs1.border.width = 1
rs1.background.color = "lightgreen"
rs1.inner_border.width = 0
rs1.inner_border.line_style = 1
rs1.inner_border.color= "red"
rs1.opacity = 0.6
rs1.hz_align = 2 # 0 left, 1 center, 2 right
rs1.vt_align = 1 # 0 left, 1 center, 2 right
br1 = faces.TextFace("branch-right1", fsize=12, fgcolor="#009000")
br2 = faces.TextFace("branch-right3", fsize=12, fgcolor="#009000")
# New face positions (branch-top and branch-bottom)
bb = faces.TextFace("branch-bottom 1", fsize=8, fgcolor="#909000")
bb2 = faces.TextFace("branch-bottom 2", fsize=8, fgcolor="#909000")
bt = faces.TextFace("branch-top 1", fsize=6, fgcolor="#099000")
# And faces can also be used as headers or foot notes of aligned
# columns
t1 = faces.TextFace("Header Face", fsize=12, fgcolor="#aa0000")
t2 = faces.TextFace("Footer Face", fsize=12, fgcolor="#0000aa")
# Attribute faces can now contain prefix and suffix fixed text
aligned = faces.AttrFace("name", fsize=12, fgcolor="RoyalBlue",
text_prefix="Aligned (", text_suffix=")")
# horizontal and vertical alignment per face
aligned.hz_align = 1 # 0 left, 1 center, 2 right
aligned.vt_align = 1
# Node style handling is no longer limited to layout functions. You
# can now create fixed node styles and use them many times, save them
# or even add them to nodes before drawing (this allows to save and
# reproduce an tree image design)
style = NodeStyle()
style["fgcolor"] = "Gold"
style["shape"] = "square"
style["size"] = 15
style["vt_line_color"] = "#ff0000"
t.set_style(style)
# add a face to the style. This face will be render in any node
# associated to the style.
fixed = faces.TextFace("FIXED branch-right", fsize=11, fgcolor="blue")
t.add_face(fixed, column=1, position="branch-right")
# Bind the precomputed style to the root node
# ETE 2.1 has now support for general image properties
ts = TreeStyle()
# You can add faces to the tree image (without any node
# associated). They will be used as headers and foot notes of the
# aligned columns (aligned faces)
ts.aligned_header.add_face(t1, column = 0)
ts.aligned_header.add_face(t1, 1)
ts.aligned_header.add_face(t1, 2)
ts.aligned_header.add_face(t1, 3)
t1.hz_align = 1 # 0 left, 1 center, 2 right
t1.border.width = 1
ts.aligned_foot.add_face(t2, column = 0)
ts.aligned_foot.add_face(t2, 1)
ts.aligned_foot.add_face(t2, 2)
ts.aligned_foot.add_face(t2, 3)
t2.hz_align = 1
# Set tree image style. Note that aligned header and foot is only
# visible in "rect" mode.
ts.mode = "r"
ts.scale = 10
for node in t.traverse():
# If node is a leaf, add the nodes name and a its scientific
# name
if node.is_leaf():
node.add_face(aligned, column=0, position="aligned")
node.add_face(aligned, column=1, position="aligned")
node.add_face(aligned, column=3, position="aligned")
else:
node.add_face(bt, column=0, position="branch-top")
node.add_face(bb, column=0, position="branch-bottom")
node.add_face(bb2, column=0, position="branch-bottom")
node.add_face(br1, column=0, position="branch-right")
node.add_face(rs1, column=0, position="branch-right")
node.add_face(br2, column=0, position="branch-right")
return t, ts
from ete_dev import Tree
t = Tree( '((H:0.3,I:0.1):0.5, A:1, (B:0.4,(C:1,D:1):0.5):0.5);' )
# Create a small function to filter your nodes
def conditional_function(node):
if node.dist > 0.3:
return True
else:
return False
# Use previous function to find matches. Note that we use the traverse
# method in the filter function. This will iterate over all nodes to
# assess if they meet our custom conditions and will return a list of
# matches.
matches = filter(conditional_function, t.traverse())
print len(matches), "nodes have ditance >0.3"
# depending on the complexity of your conditions you can do the same
# in just one line with the help of lambda functions:
matches = filter(lambda n: n.dist>0.3 and n.is_leaf(), t.traverse() )
print len(matches), "nodes have ditance >0.3 and are leaves"
# faces.add_face_to_node(f, node, 0, position="branch-right")
f.border.width = 0
# node.img_style["bgcolor"] = random_color()
# Tree().show()
ts = TreeStyle()
ts.mode = "c"
ts.layout_fn = layout
ts.show_leaf_name = False
ts.arc_span = 340
ts.arc_start = -70
# ts.allow_face_overlap = True
# ts.show_branch_length = True
ts.draw_guiding_lines = False
ts.optimal_scale_level = "mid"
ts.extra_branch_line_color = "red"
ts.root_opening_factor = 0.50
ts.show_border = True
ts.scale = None
t = Tree()
t.populate(200, random_branches=True, branch_range=(0, 0))
t.dist = 0.0
dists = [n.dist for n in t.traverse() if n.dist != 0]
# print max(dists), min(dists)
t.write(outfile="test.nw")
# for s in [5, None]:
# ts.scale = s
# t.render("img_scale_%s.png" %s, tree_style = ts, w=600)
t.show(tree_style=ts)